Gaussian Mixture Model-based Method for Estimating Impedance Parameters of Phaseless Transmission Lines
The accurate estimation of impedance parameters of power transmission lines is the foundation for reliable analysis of distribution networks.Existing methods for estimating line impedance require the utilization of phase information from measured voltage and current.However,these methods become ineffective when the phase angle of the measured data is missing.Therefore,this paper proposes a phaseless line impedance parameter estimation method based on a Gaussian mixture model,which only requires the measured data of voltage magnitude,active power,and reactive power at both ends of the line.First an error-inclusive line impedance estimation model is established based on power flow relationships of the power transmission line.Then considering the noise interference in the measurement environment,a Gaussian mixture model is established by introducing dual hidden variables,transforming the error-inclusive line impedance estimation model into a Gaussian mixture model.The probability distribution of the line impedance is obtained,and the estimation values of the line impedance parameters are obtained using the maximum a posteriori estimation.Finally,through simulations,it is demonstrated that the proposed method can effectively estimate the magnitude of the line impedance,providing a new strategy for line impedance estimation in scenarios with missing phase information.
electrical line impedanceGaussian mixture modelmissing phase angles in measured datanoise errormaximum a posteriori estimation
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